1. Field of the Invention
The present invention relates to history matching in petroleum reservoir simulations, and more particularly obtaining information about changes in fluid displacement in reservoirs of onshore fields where surface time-lapse seismic surveys cannot be readily performed.
2. Description of the Related Art
Generating accurate production forecasts is an area of major concern for the oil industry. Using uncertain production and reserves forecasts for investment decisions leads to a significant risk of sub-optimal performance. It is difficult and expensive to generate consistent models to accurate represent oil fields due to geological complexities, the size of the reservoir model, and the amount of data being produced. For a large reservoir, such as the type known in the industry as a giant reservoir, the number of grid cells can often exceed hundreds of millions. This number of cells is required in order to have adequate resolution to represent flow dynamics, formation rock porosity and permeability heterogeneity, and many other geologic and depositional complexities within the reservoir. Needless to say, a reservoir model is a complex dynamic system.
Regardless of the stage of development of a field, its representation is equally challenging. A new field starting production has limited amounts of data. Traditionally, few wells are put to production in the early stages of development. Thus, little information is known, and a reliable reservoir model can only be properly built after several years of production data are acquired.
For a mature reservoir, on the other hand, there is a vast amount of data which presents a challenge to a reservoir engineer. A mature reservoir can have in excess of a hundred producing wells, millions of active grid cells and production data gathered over many decades.
Usually, present reservoir studies start by the acquisition of 3D seismic data at the early stages of development and appraisal to obtain a clear picture of the field. This dataset is used to give the exploration and production teams a wider view of the field, reducing development uncertainties, and providing a better understanding of how to develop the field for optimum production. Only few wells are usually drilled in early stages, due to economic constrains. This provides a limited amount of information about the petrophysical properties of the rocks, pressure, saturation, rock composition and fluid composition, which are only known at the well locations. These limited amounts of information are used to create a first reservoir model. The model is not static and it is continuously updated as more information becomes available through a process known as history matching.
History matching is an important tool to obtain more accurate models to describe the reservoir, thus improving the capability of producing accurate forecasts. This is highly related to good reservoir management practices. Until recently, history matching was done by updating the reservoir model parameters to match historical production data. This was often an underdetermined inverse problem with multiple solutions. Different models might fit the given production data regardless of whether or not they were geologically accurate.
During the production stage from wells in a reservoir, not only the oil production rate (OPR) is measured, the gas oil rate (GOR) and the water cut (WCT) are also measured as these are important measures. The oil production rate represents the gross revenue obtainable, while high GOR and WCT measures indicate factors which reduce potential income.
Fluid samples and the static bottomhole pressure (SBHP) are also measured at selected wells during production to ensure consistency with the development plan and simulations being carried out. The measured pressure contains information about the reservoir continuity, fluid contacts and its depletion mechanism. These parameters are taken into consideration during the history matching process.
Conventional history matching was designed as a trial and error process making it a complex and time consuming task. Considerable investment has been made in recent years to improve history matching practices. Computer-assisted methods have been under development to help reservoir engineers explore new complex geological areas and to efficiently deal with ever-increasing amounts of data being produced.
Among the most successful methods employed to condition reservoir models to production data are conjugate gradient optimization methods and ensemble based Bayesian filtering methods. Conjugate gradient methods require the calculation of the gradient of an objective function measuring the model fit with the data. This is a challenging task because it requires developing and running the adjoint code of the reservoir model.
A recent advancement in history matching is the use of time-lapse seismic data to help understand the fluid displacement in the reservoir. Time-lapse seismic data, also known as 4D seismic data, provides more spatial coverage than other reservoir data sets. Time-lapse seismic or 4D seismic data have been used with success in the oil industry to improve reservoir understanding, which in turn has great importance for reservoir management applications. The qualitative use of 4D seismic in its simplest form allows the identification of undrained areas “hot spots”, allowing reservoir engineers to properly design better management practices to improve oil recovery.
4D seismic surveying is performed by repeatedly shooting 3D seismic surveys with arrays of closely spaced receivers and shot lines at the surface over the same area at different times. The infill fluids present in the reservoir rocks have different acoustic impedances. The difference between two seismic surveys over time indicated by changes of acoustic impedance can then be used to highlight unexplored compartments and track movements of flood fronts.
Qualitative approaches have been implemented in the past decade to use 4D seismic data sets, allowing development possibilities including better well placement and fluid drainage. The use of more sophisticated quantitative approaches has recently become possible. However, because of the nature and complexity of the data, which can exceed by several orders of magnitude the multiple millions of grid cells in a large reservoir, most of the research performed so far only dealt with inverted seismic data for elastic parameters, mainly acoustic impedance and Poisson's ratio. Other seismic and elastic attributes were at times also used.
To be considered a good candidate for a time-lapse study the analyzed data needs to show significant variation over production activity. Density variations due to fluid saturation changes are minimal, usually in the range of 1%, thus they cannot be accounted for due to noise and uncertainty levels. The major variation occurs in the velocity field and consequently bulk modulus of the rocks.
Other studies have tested alternative approaches to include time-lapse seismic in history matching studies. One approach was to directly assimilate seismic data, before inversion and in amplitude form. J. A. Skjervheim and B. O. Ruud, Combined Inversion of 4D Seismic Waveform Data And Production Data Using Ensemble Kalman Filter, 2006. A similar approach extended this to a 3D reservoir. O. Leeuwenburgh, J. Brouwer, and M. Trani, Ensemble-Based Conditioning Of Reservoir Models To Seismic Data, Computational Geosciences, 2011, 15(2): p. 359-378. Another approach assimilated reparameterized seismic data in terms of arrival times at the interpreted fluid fronts. M. Trani, R. Arts, and O. Leeuwenburgh, Seismic History Matching of Fluid Fronts Using the Ensemble Kalman Filter, SPE Journal, 2013, 18(1): p. 159-171.
In addition to the processing complexities above for reservoirs in general, the technology of time lapse seismic data acquisition faces major challenges in onshore fields. In onshore fields, it is almost impossible in most cases to acquire time-lapse data. This occurs mainly because changes in the surface environment (construction of surface facilities, urban expansion, alterations in the environment and other reproducibility issues). These changes over time in the surface environment become part of the data content of the 4D surveys, and thus prevent observing differences between 4D seismic surveys to monitor reservoir performance. This is a primary reason that many oil producing companies do not have 4D seismic data for their onshore oil fields.